1Z0-1127-25 LLM Fundamentals Practice Question
A data scientist is building a text summarization system using an LLM. They want to evaluate the model's output against human-written summaries. Which TWO metrics are most appropriate for this evaluation? (Choose two.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
ROUGE
ROUGE is recall-oriented and measures overlap of n-grams, making it a standard metric for summarization. BERTScore measures semantic similarity using embeddings, which can capture meaning even when wording differs. BLEU is more for translation, perplexity measures fluency, and human evaluation is qualitative but not a metric.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Human evaluation rubrics
Why it's wrong here
Human evaluation is a method, not a metric; the question asks for metrics.
- ✗
BLEU
Why it's wrong here
BLEU is precision-oriented and more commonly used for machine translation, not summarization.
- ✓
ROUGE
Why this is correct
ROUGE is recall-oriented and widely used for summarization evaluation.
- ✗
Perplexity
Why it's wrong here
Perplexity measures how well the model predicts the next token, not summary quality.
- ✓
BERTScore
Why this is correct
BERTScore provides semantic similarity evaluation, which is useful for summarization.
Go deeper
Related to this question
About these practice questions
Courseiva writes every 1Z0-1127-25 question from scratch — 768 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This 1Z0-1127-25 practice question is part of Courseiva's free Oracle certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 1Z0-1127-25 exam.